title: |
Combing Extended Kalman Filters and Support Vector Machines for Online Option Price Forecasting |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-01-7 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/jcis.2006.53 (how to use a DOI) | |
author(s): |
Shian-chang Huang |
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corresponding author: |
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publication date: |
October 2006 |
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keywords: |
Online forecast, Extended Kalman filter, Support vector machine, Feedforward neural network |
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abstract: |
This study combines extended Kalman filters (EKFs) and support vector machines (SVMs) to implement a fast online predictor for option prices. The EKF is used to infer latent variables and makes a prediction based on the Black-Scholes formula, while the SVM is employed to capture the nonlinear residuals between the actual option prices and the EKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting
accuracy of the proposed model, and found that the hybrid model is superior to traditional feedforward neural network models, which can significantly reduce the root-mean-squared forecasting errors. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |